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51. Lecture 12. D) Wald Test‏‎ (used on 1 page)
52. Lecture 14. C) Convergence in Distribution‏‎ (used on 1 page)
53. Lecture 16. G) Multiple Observations‏‎ (used on 1 page)
54. Lecture 4. B) Bernoulli‏‎ (used on 1 page)
55. Lecture 6. G) Some Inequalities‏‎ (used on 1 page)
56. Lecture 1. E) More on Probability Functions‏‎ (used on 1 page)
57. Lecture 12. E) Example: LRT‏‎ (used on 1 page)
58. Lecture 14. D) Slutsky’s Theorem‏‎ (used on 1 page)
59. Lecture 16. H) Theorem: Berstein von-Mises‏‎ (used on 1 page)
60. Lecture 4. C) Binomial‏‎ (used on 1 page)
61. Lecture 7. A) Random Sample‏‎ (used on 1 page)
62. Lecture 1. F) Random Variables‏‎ (used on 1 page)
63. Lecture 12. F) Test Equivalence‏‎ (used on 1 page)
64. Lecture 14. E) Central Limit Theorem‏‎ (used on 1 page)
65. Lecture 17. A) Ordinary Least Squares‏‎ (used on 1 page)
66. Lecture 4. D) Poisson‏‎ (used on 1 page)
67. Lecture 7. B) Statistics‏‎ (used on 1 page)
68. Lecture 10. A) Finding UMVU Estimators‏‎ (used on 1 page)
69. Lecture 12. G) Equivalence Between LRT and LM Tests‏‎ (used on 1 page)
70. Lecture 14. F) Delta Method‏‎ (used on 1 page)
71. Lecture 17. B) Normal Linear Model‏‎ (used on 1 page)
72. Lecture 4. E) Uniform‏‎ (used on 1 page)
73. Lecture 7. C) Order Statistics‏‎ (used on 1 page)
74. Lecture 10. B) Complete Statistic‏‎ (used on 1 page)
75. Lecture 12. H) Equivalence Between LRT and Wald Tests‏‎ (used on 1 page)
76. Lecture 14. G) Somewhat Pedantic Remark on Notation‏‎ (used on 1 page)
77. Lecture 17. C) Asymptotic Properties of OLS‏‎ (used on 1 page)
78. Lecture 4. F) Gamma‏‎ (used on 1 page)
79. Lecture 7. D) Statistical Inference‏‎ (used on 1 page)
80. Lecture 10. C) Cramer-Rao Lower Bound‏‎ (used on 1 page)
81. Lecture 12. I) Optimal Tests‏‎ (used on 1 page)
82. Lecture 15. A) Asymptotic Properties of ML Estimators‏‎ (used on 1 page)
83. Lecture 17. D) Bootstrapping‏‎ (used on 1 page)
84. Lecture 4. G) Normal‏‎ (used on 1 page)
85. Lecture 8. A) Point Estimation‏‎ (used on 1 page)
86. Lecture 11. A) Hypothesis Testing‏‎ (used on 1 page)
87. Lecture 12. J) Neyman-Pearson Lemma‏‎ (used on 1 page)
88. Lecture 15. B) Some Implications‏‎ (used on 1 page)
89. Lecture 18. A) Multicollinearity‏‎ (used on 1 page)
90. Lecture 4. H) Dirac delta function‏‎ (used on 1 page)
91. Lecture 8. B) Method of Moments‏‎ (used on 1 page)
92. Lecture 11. B) Testing Procedure‏‎ (used on 1 page)
93. Lecture 13. A) Test Optimality (cont.)‏‎ (used on 1 page)
94. Lecture 15. C) Example: Hypothesis Test‏‎ (used on 1 page)
95. Lecture 18. B) Partitioned Regression‏‎ (used on 1 page)
96. Lecture 5. A) Families of Distributions‏‎ (used on 1 page)
97. Lecture 8. C) Maximum Likelihood‏‎ (used on 1 page)

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